Changes to a subject which occur over an extended period of time (i.e., also referred to as longitudinal changes) can be measured using highly accurate segmentation and volumetric measurements of structures of the subject. For example, when measuring longitudinal changes in brain structure, segmentation and volumetric measurements of the brain structure may be completed, either manually or by labeled atlas warping, from images acquired at different time-points. Segmentation methods typically fall into two categories, manual segmentation methods and automatic/semi-automatic segmentation methods. Manual segmentation may include extensive human interaction and considerable training of an individual (rater) providing the manual segmentation. Intra-rater reproducibility and inter-rater agreement are difficult to achieve in a longitudinal study in which manual segmentation is used, particularly when small longitudinal changes are to be measured. This has led to the development of automatic/semi-automatic image segmentation and parcelation methods, often based on atlas matching and registration. Furthermore, there are two categories of automatic registration methods. The first category includes methods based on feature matching, in which spatial transformations are calculated from a number of distinct features, for example, anatomical features, and correspondences are established either manually, semi-automatically, or fully automatically for these distinct anatomical features. The distinct anatomical features are distinct landmark points or a combination of curves and surfaces, for example, in a morphological brain these distinct anatomical features may be, for example, sulci or gyri. The second category includes methods based on volumetric transformations to maximize a similarity between a subject and a template, and generally assume that the subject and the template are acquired by a common imaging protocol.
All of the above-mentioned warping methods are mainly designed for three dimensional (hereafter sometimes referred to as 3D) images. Consequently, applying these warping methods independently for each time-point in a longitudinal study of a plurality of 3D images typically leads to noisy longitudinal measurements, particularly, for small structures such as the hippocampus, due to inconsistencies in atlas matching among different time-points. Smoothness in the longitudinal measurements (i.e., measurement taken over an extended time period) may be generally assumed, as long as longitudinal images of the subject are collected with adequate temporal resolution. Although a smooth estimation of the longitudinal changes may be obtained by smoothing the measurements along the temporal dimension, the smoothed measurements, in general, can significantly deviate from actual image data, unless the smoothing is performed concurrently with the warping and, thus, takes into consideration the image features.
What is needed is a method and apparatus which overcomes the above-mentioned problems.